Fetch and normalize public RSS or Atom feeds into stable structured JSON entries for agents and research workflows.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Structured Feed normalization result | |
| meta | Yes | ||
| service | Yes | ||
| version | Yes | ||
| request_id | Yes | Unique request identifier |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly and idempotent. The description adds value by noting 'public' feeds (no auth) and the promise of 'stable structured JSON entries', which is a behavioral guarantee beyond the schema. It does not mention rate limits or failure handling, but the bar is lower with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence of 16 words, front-loaded with the action and resource, and contains no filler or redundant details. It is appropriately concise for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists and annotations are strong, the description covers the core purpose and output type. It lacks parameter semantics and alternative differentiation, but for a fetch-and-normalize tool with two self-explanatory parameters, it is largely complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for parameter meaning, but it does not mention 'url' or 'limit' at all. The schema itself provides types, defaults, and formats, so the agent can guess, but the description adds no semantic value beyond property names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verbs ('Fetch', 'normalize') and a clear resource ('public RSS or Atom feeds'), and specifies the output ('stable structured JSON entries'). This distinguishes it from sibling tools like research.news-aggregate or web.extract.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for 'agents and research workflows' but provides no explicit when-to-use vs alternatives, no exclusions, and does not name alternative tools. It conveys the general context but lacks comparative guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools are grouped into clear domain prefixes (crypto, data, developer, document, research, web) and each tool name describes a specific function; however, a few umbrella tools like web.full-audit and data.contract overlap with their more targeted counterparts, creating minor ambiguity.
All tool names follow a consistent pattern: a domain prefix, a dot, and a hyphenated lowercase compound name (e.g., crypto.base-block-inspect, web.seo-audit). This makes naming predictable and easy to scan.
At 63 tools, the surface area is very large and exceeds the 50+ threshold for extreme mismatch. While the tools are organized into six domains, the sheer number makes it difficult for an agent to select efficiently, and some tools are bundled combinations of others.
Each domain offers a thorough set of operations: crypto covers address, account, block, contract, events, gas, and transaction inspection; data covers cleaning, conversion, schema, and validation; developer covers code review, dependency/license audits, and test generation; research covers SEC, OFAC, GLEIF, and USAspending; web covers extraction, SEO, security, and performance. No obvious dead ends exist for the read-only/inspection purpose.